☆ 4.6 Article

Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)

SUSTAINABILITY (2023)

Related references

Note: Only part of the references are listed.

Review Environmental Sciences

Air pollution and liver cancer: A systematic review

Ting Gan et al.

Summary: This article reviews the association between air pollution and liver cancer, finding that PM2.5 is associated with liver cancer mortality, while other air pollutants are not. Future research is needed to further evaluate the impact of multiple air pollutants on liver cancer in different socio-environmental contexts.

JOURNAL OF ENVIRONMENTAL SCIENCES (2023)

Add to Collection

Article Mathematics, Applied

Hourly pollutants forecasting using a deep learning approach to obtain the AQI

Jose Antonio Moscoso-Lopez et al.

Summary: This study aims to forecast the Air Quality Index (AQI) by predicting pollutant concentrations using artificial neural networks and long short-term memory networks. Two input approaches were evaluated, and the results showed that the method of adding exogenous variables improved the performance of the prediction models. The newly proposed method LSTMNA provided the best performances in most cases evaluated.

LOGIC JOURNAL OF THE IGPL (2023)

Add to Collection

Article Environmental Sciences

Data-driven predictive modeling of PM2.5 concentrations using machine learning and deep learning techniques: a case study of Delhi, India

Adil Masood et al.

Summary: The present study aims to forecast PM2.5 concentration in a location in Delhi using machine learning and deep learning models. The LSTM model is found to be the best performing technique followed by MLFFNN, SVM, and RF models. The analysis also identifies key parameters influencing PM2.5 estimation.

ENVIRONMENTAL MONITORING AND ASSESSMENT (2023)

Add to Collection

Article Environmental Sciences

Air pollution relevance analysis in the bay of Algeciras (Spain)

M. Rodriguez-Garcia et al.

Summary: The aim of this study is to conduct an in-depth analysis of air pollution in the two main cities of Bay of Algeciras, Spain. Data from 2010-2015 on air pollutant concentrations and weather measurements were collected, revealing higher levels of nitrogen dioxide, sulphur dioxide, and particulate matter in Algeciras and La Linea. Equivalent stations were identified through trends analysis, allowing for substitution of data in case of failure.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2023)

Add to Collection

Article Environmental Sciences

Potential ambient NO2 abatement by applying photocatalytic materials in a Spanish city and analysis of short-term effect on human mortality

Jaime Fernandez-Pampillon et al.

Summary: Road traffic is the main source of NO2 emissions in European cities, surpassing the limit values for human health protection. The effectiveness of photocatalytic compounds with titanium dioxide (TiO2) in reducing NO2 is being investigated. This study evaluates the potential of TiO2-based materials in removing NO2 in a specific street in Alcobendas, Spain and estimates the number of avoided deaths due to NO2 exposure. The implementation of photocatalytic materials in the urban environment shows a marginal reduction in mortality and a potential cost saving in transport-related damage.

ENVIRONMENTAL POLLUTION (2023)

Add to Collection

Review Environmental Sciences

A systematic review of evidence for maternal preconception exposure to outdoor air pollution on Children's health

Natalie Blanc et al.

Summary: The preconception period is a critical window for gametogenesis, and exposure to air pollutants during this period can have long-term effects on children's health outcomes. A systematic review of epidemiological evidence found that outdoor air pollution during the maternal preconception period was associated with various health outcomes, with birth defects showing the most consistent findings. Future studies should improve exposure assessment methods and integrate pathophysiologic biomarkers to enhance the understanding of the causal relationship between preconception exposure and health outcomes.

ENVIRONMENTAL POLLUTION (2023)

Add to Collection

Article Environmental Sciences

Combined model of air quality index forecasting based on the combination of complementary empirical mode decomposition and sequence reconstruction

Weijun Wang et al.

Summary: This study introduces a novel integrated forecasting model for the air quality index (AQI) to provide reliable predictions, which can be used as references for urban air pollution control, public health construction, and residents' travel planning. The original data is decomposed using complementary set empirical mode decomposition (CEEMD) and reconstructed using the fuzzy entropy (FE) algorithm. The combined forecasting model is established and different prediction methods are used for different frequency sequences. Empirical analysis on Beijing and Chengdu shows that the proposed CEEMD-FE-WOA-LSTM-ELM model has the highest prediction accuracy among all the comparison models.

ENVIRONMENTAL POLLUTION (2023)

Add to Collection

Article Environmental Sciences

An approach to forecast pollutants concentration with varied dispersion

B. Deep et al.

Summary: The escalating air pollution globally is a serious concern, leading to the necessity of predicting pollution levels in advance. The authors developed a predictive model using RNN and Bi-LSTM to accurately forecast pollutant concentrations, catering to different dispersion patterns in various cities. The model's efficacy was validated through testing on real data and performance metrics evaluation.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Simple Models in Complex Worlds: Occam's Razor and Statistical Learning Theory

Falco J. Bargagli Stoffi et al.

Summary: This article discusses the relationship between simplicity and truth, as well as how to balance simplicity and accuracy in supervised machine learning. The study found that in certain situations, excessive pursuit of simplicity actually slows down the learning process. These results have important implications for both machine learning and the philosophy of science.

MINDS AND MACHINES (2022)

Add to Collection

Article Green & Sustainable Science & Technology

Attention-Based Distributed Deep Learning Model for Air Quality Forecasting

Axel Gedeon Mengara Mengara et al.

Summary: This paper proposes an attention-based convolutional BiLSTM autoencoder model for worldwide air quality prediction, with Busan, South Korea as the model city. The model outperforms previous models in short- and long-term predictions, and traffic information can improve the accuracy of air quality forecasting. Additionally, the model has lower training time and has been successfully deployed in a cloud server for real-time air quality information.

SUSTAINABILITY (2022)

Add to Collection

Article Geochemistry & Geophysics

Sustainable optimized LSTM-based intelligent system for air quality prediction in Chennai

Sridhar Gunasekar et al.

Summary: “This study proposes a novel optimized deep learning algorithm for efficient air quality prediction in Chennai, India. By combining multiple models including ARIMA and CNN-LSTM with the Tuna Optimization Algorithm, the proposed method achieves higher accuracy in predicting air quality. The results show significant improvements in various metrics compared to existing models.”

ACTA GEOPHYSICA (2022)

Add to Collection

Article Green & Sustainable Science & Technology

PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model

Wenchao Ban et al.

Summary: This paper proposes a method combining the CEEMDAN algorithm with deep learning hybrid models for PM2.5 concentration prediction. The method decomposes the PM2.5 data into multiple modal components using CEEMDAN algorithm, and applies appropriate models to each component for prediction, and then combines the prediction results to obtain the final result. Experimental results show that the coupled CEEMDAN-LSTM-BP-ARIMA model has the best prediction ability, and data with different characteristics need to be predicted using different models.

SUSTAINABILITY (2022)

Add to Collection

Article Green & Sustainable Science & Technology

The Influence of Emissions from Maritime Transport on Air Quality in the Strait of Gibraltar (Spain)

Vanessa Duran-Grados et al.

Summary: This study estimates the influence of NOx, SOx, and particulate matter on air quality in the Strait of Gibraltar, and identifies the Algeciras Bay Industrial Zone as having the highest pollutant levels and the Palmones area as a major hotspot. Maritime traffic emissions have a significant impact on particle emissions, CO, HC, NMVOC, and CO2 concentrations.

SUSTAINABILITY (2022)

Add to Collection

Article Engineering, Chemical

Forecasting of Air Quality Using an Optimized Recurrent Neural Network

Khawaja Hassan Waseem et al.

Summary: Clean air is crucial for a healthy life. This research successfully forecasted PM2.5 concentrations in different cities in Pakistan using advanced deep learning and machine learning models, highlighting the importance of a data-driven approach in addressing air pollution.

PROCESSES (2022)

Add to Collection

Article Energy & Fuels

Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland

Justyna Kujawska et al.

Summary: This study compared machine learning models for predicting PM10 levels in the air in the city of Lublin and found that the Artificial Neural Network (ANN) model performed slightly better than other models. By using the ANN model to predict PM10 levels, the risk of exceeding the alert level and the air quality in the region can be assessed.

ENERGIES (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

A permutation entropy-based EMD-ANN forecasting ensemble approach for wind speed prediction

J. J. Ruiz-Aguilar et al.

Summary: Accurate wind speed prediction is crucial for various tasks, especially air pollution modeling. A hybrid wind speed forecasting approach was proposed in this study, combining different methods to achieve higher prediction performance. Experimental results showed that the EMD-PE-ANN model outperformed single ANN models in all tested prediction horizons, indicating that it could be a powerful tool for wind speed forecasting.

NEURAL COMPUTING & APPLICATIONS (2021)

Add to Collection

Editorial Material Environmental Sciences

Recent Advances in Studying Air Quality and Health Effects of Shipping Emissions

Daniele Contini et al.

Summary: The growth of the shipping industry has led to negative impacts on health and climate, particularly in coastal areas. While maritime activities drive economic and social development, they also impose environmental pressures. Policies have been put in place over the past decades to reduce sulfur content in marine fuels.

ATMOSPHERE (2021)

Add to Collection

Article Chemistry, Analytical

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Javier Gonzalez-Enrique et al.

Summary: This study aims to accurately predict NO2 concentrations at a specific station using artificial neural networks and LSTM models. A new prediction method was proposed, combining LSTMs with a rolling window scheme and cross-validation for time series. The inclusion of exogenous variables improved model performance, particularly for forecast horizons of t + 4 and t + 8. The LSTM-CVT method showed promising results and outperformed ANNs in error values in 85% of cases.

SENSORS (2021)

Add to Collection

Article Engineering, Environmental

A comparison of ranking filter methods applied to the estimation of NO2 concentrations in the Bay of Algeciras (Spain)

Javier Gonzalez-Enrique et al.

Summary: This study compares sixteen filter ranking methods for a real air pollution problem, finding that mRMR methods outperformed others when selecting a small number of features, but their advantage diminished as the number of features increased.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2021)

Add to Collection

Article Construction & Building Technology

An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach

K. Krishna Rani Samal et al.

Summary: Air pollution forecasting plays a crucial role in controlling pollution's impact on the environment and public health. The proposed deep learning model can improve prediction accuracy, recover missing values, conduct temporal modeling, and reveal the correlation between particulate matter and meteorological factors. Experimental results in Talcher, India, and Beijing, China show that the model outperforms other state-of-the-art and baseline models.

SUSTAINABLE CITIES AND SOCIETY (2021)

Add to Collection

Article Ecology

Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique

Sheen Mclean Cabaneros et al.

ECOLOGICAL MODELLING (2020)

Add to Collection

Article Environmental Sciences

An LSTM-based aggregated model for air pollution forecasting

Yue-Shan Chang et al.

ATMOSPHERIC POLLUTION RESEARCH (2020)

Add to Collection

Article Green & Sustainable Science & Technology

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Thanongsak Xayasouk et al.

SUSTAINABILITY (2020)

Add to Collection

Review Environmental Sciences

Deep Learning for Air Quality Forecasts: a Review

Qi Liao et al.

CURRENT POLLUTION REPORTS (2020)

Add to Collection

Article Environmental Sciences

Shipping emissions in the Iberian Peninsula and the impacts on air quality

Rafael A. O. Nunes et al.

ATMOSPHERIC CHEMISTRY AND PHYSICS (2020)

Add to Collection

Article Engineering, Environmental

Spatial and meteorological relevance in NO2 estimations: a case study in the Bay of Algeciras (Spain)

Javier Gonzalez-Enrique et al.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2019)

Add to Collection

Proceedings Paper Engineering, Electrical & Electronic

Internet of Things Solution for Intelligent Air Pollution Prediction and Visualization

Mladen Korunoski et al.

PROCEEDINGS OF 18TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES (IEEE EUROCON 2019) (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction

Junshan Wang et al.

NEUROCOMPUTING (2018)

Add to Collection

Article Engineering, Environmental

Forecasting air quality time series using deep learning

Brian S. Freeman et al.

JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (2018)

Add to Collection

Review Environmental Sciences

Shipping emissions and their impacts on air quality in China

Yan Zhang et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2017)

Add to Collection

Article Environmental Sciences

The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain

Ibai Lana et al.

ATMOSPHERIC ENVIRONMENT (2016)

Add to Collection

Article Engineering, Environmental

Spatial and Seasonal Dynamics of Ship Emissions over the Yangtze River Delta and East China Sea and Their Potential Environmental Influence

Qianzhu Fan et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2016)

Add to Collection

Article Thermodynamics

Methodologies for estimating shipping emissions and energy consumption: A comparative analysis of current methods

Juan Moreno-Gutierrez et al.

ENERGY (2015)

Add to Collection

Article Environmental Sciences

Relationships between meteorological parameters and criteria air pollutants in three megacities in China

Hongliang Zhang et al.

ENVIRONMENTAL RESEARCH (2015)

Add to Collection

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Add to Collection

Article Environmental Sciences

Sulfur Dioxide Emission Estimates from Merchant Vessels in a Port Area and Related Control Strategies

Ta-Kang Liu et al.

AEROSOL AND AIR QUALITY RESEARCH (2014)

Add to Collection

Article Meteorology & Atmospheric Sciences

Contribution of ship emissions to the fine particulate in the community near an international port in Hong Kong

P. S. Yau et al.

ATMOSPHERIC RESEARCH (2013)

Add to Collection

Article Meteorology & Atmospheric Sciences

A refined index of model performance

Cort J. Willmott et al.

INTERNATIONAL JOURNAL OF CLIMATOLOGY (2012)

Add to Collection

Article Environmental Sciences

Ultrafine particles pollution in urban coastal air due to ship emissions

Yenny Gonzalez et al.

ATMOSPHERIC ENVIRONMENT (2011)

Add to Collection

Article Environmental Sciences

Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain:: A multiple comparison strategy

Ignacio J. Turias et al.

ENVIRONMENTAL MONITORING AND ASSESSMENT (2008)

Add to Collection

Article Environmental Sciences

Identification and characterization of inland ship plumes over Vancouver, BC

G Lu et al.

ATMOSPHERIC ENVIRONMENT (2006)

Add to Collection

© Peeref 2019-2024. All rights reserved.